DOCSLIB.ORG

Analysis of Molecular Mechanisms in Intestinal Stem Cells and Colorectal

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Analysis of Molecular Mechanisms in Intestinal Stem Cells and Colorectal JENNY HÖGSTRÖM-STAKEM Analysis of Molecular Pathways in Intestinal Stem Cells and Colorectal Cancer Progression JENNY HÖGSTRÖM-STAKEM Analysis of Molecular Pathways in Recent Publications in this Series 67/2018 Lasse Karhu Computational Analysis of Orexin Receptors and Their Interactions with Natural and Synthetic Ligands 68/2018 Hanna Pitkänen Alterations and Impact of Thrombin Generation and Clot Formation in Solvent/Detergent Plasma, FXIII Deficiency and Lysinuric Protein Intolerance DISSERTATIONES SCHOLAE DOCTORALIS AD SANITATEM INVESTIGANDAM 69/2018 Riikka Havunen UNIVERSITATIS HELSINKIENSIS 87/2018 Enhancing Adoptive Cell Therapy of Solid Tumours with Armed Oncolytic Adenoviruses 70/2018 Patrick Penttilä Novel Biomarkers in Metastatic Renal Cell Carcinoma 71/2018 Jaakko Keinänen Metabolic Changes, Inflammation and Mortality in Psychotic Disorders 72/2018 Heidi Marjonen JENNY HÖGSTRÖM-STAKEM Effects of Prenatal Alcohol Exposure on the Epigenome, Gene Expression and Development 73/2018 Dyah Listyarifah The Role of the Oral Spirochete Treponema denticola in Periodontitis and Orodigestive Analysis of Molecular Pathways in Intestinal Stem Carcinogenesis Cells and Colorectal Cancer Progression 74/2018 Teija-Kaisa Aholaakko Intraoperative Aseptic Practices and Surgical Site Infections in Breast Surgery 75/2018 Cecilia Anna Brunello Tau Pathology: Secretion and Internalization as the Key for Understanding Protein Propagation 76/2018 Niko M. Perttilä Exercise and Falls among Frail Older People – Special Focus on People with Dementia 77/2018 Hanna Knihtilä Assessment of Small Airway Function – Application of Impulse Oscillometry in Young Children with Asthmatic Symptoms 78/2018 Mikko Jalanko Alterations in Myocardial Function and Electrocardiology in Hypertrophic Cardiomyopathy 79/2018 Jenni Lehtisalo Diet, Diabetes, and Prevention of Cognitive Decline — Focus on Lifestyle Intervention 80/2018 Maria Sanz Navarro Sox2 and Meis1 in Tooth Development and Murine Incisor Renewal 81/2018 Anna Boström Evaluation of Epaxial Muscle Structure in Dogs with Spinal Disease 82/2018 Anna-Liina Rahikainen Post-mortem Pharmacogenetics — Citalopram-positive Suicides as a Model Population 83/2018 Santeri Rouhinen Role of Oscillations in Visual Perception: Attention and Working Memory 84/2018 Omar Youssef Analysis of Gene Mutations in Exhaled Breath Condensate from Healthy and Lung Cancer Individuals and Profiling of Mutations and Gut Microbiota in Stools from Patients with Gastrointestinal Neoplasms RESEARCH PROGRAMS UNIT 85/2018 Teija Kasteenpohja Treatment Seeking, Treatment Adequacy and Outcome of Depressive and Anxiety Disorders TRANSLATIONAL CANCER BIOLOGY PROGRAM among Young Adults in Finland — Findings from a Population-Based Sample FACULTY OF MEDICINE 86/2018 Farhana Jahan DOCTORAL PROGRAMME IN BIOMEDICINE Phosphorylation of the α- and β-Chains of LFA-1 Regulates its Interaction with Cytoplasmic Proteins and Crosstalk to VLA-4 Integrin UNIVERSITY OF HELSINKI 87/2018 Helsinki 2018 ISSN 2342-3161 ISBN 978-951-51-4672-4 Analysis of molecular pathways in intestinal stem cells and colorectal cancer progression Jenny Högström-Stakem Research Programs Unit Translational Cancer Biology Faculty of Medicine& Doctoral Programme in Biomedicine (DPBM) University of Helsinki, Finland ACADEMIC DISSERTATION To be publicly discussed with the permission of the Faculty of Medicine, University of Helsinki, in Lecture Hall 3, Biomedicum Helsinki (Haartmaninkatu 8, Helsinki), on December 17th 2018, at 1 pm. Helsinki 2018 Supervisor Academy Professor Kari Alitalo Research Program Unit Translational Cancer Biology University of Helsinki Finland Thesis committee Associate Professor Pekka Katajisto Professor Timo Otonkoski Institute of Biotechnology Molecular Neurology and Biomedicum University of Helsinki Stem Cell Center Finland University of Helsinki Finland Reviewers appointed by the Faculty Professor Cecilia Sahlgren Docent Keijo Viiri Turku Center for Biotechnology Faculty of Medicine and Life Sciences Åbo Akademi and University of Turku University of Tampere Finland Finland Opponent appointed by the Faculty Associate Professor Kim Jensen Biotech Research & Innovation Centre University of Copenhagen Denmark ISBN 978-951-51-4672-4 (paperback) ISBN 978-951-51-4673-1 (PDF) ISSN 2342-3161 (print) ISSN 2342-317X (online) Dissertationes Scholae Doctoralis Ad Sanitatem Investigandam Universitatis Helsinkiensis No. 87/2018 Cover layout by Anita Tienhaara Hansaprint Oy Helsinki 2018 2 “It’s not whether you get knocked down, it’s whether you get up” - Vince Lombardi Dedicated to my beloved family 3 TABLE OF CONTENTS TABLE OF CONTENTS ................................................................................................................... 4 LIST OF ORIGINAL PAPERS ........................................................................................................ 5 ABBREVIATIONS ............................................................................................................................ 6 ABSTRACT ........................................................................................................................................ 8 INTRODUCTION .............................................................................................................................. 9 REVIEW OF THE LITTERATURE ............................................................................................. 10 1. SIGNALLING PATHWAYS DYSREGULATED IN COLORECTAL CANCER........................................... 10 1.1 The Wnt pathway ................................................................................................................ 10 1.2 KRAS AND MAPK/ERK signalling .................................................................................... 12 1.3 p53 pathway ........................................................................................................................ 12 1.4 The Notch pathway ............................................................................................................. 13 1.5 The TGFβ pathway ............................................................................................................. 15 2. ORGANIZATION OF THE INTESTINE ............................................................................................. 16 2.2 Regulation of stemness and differentiation ........................................................................ 17 2.2.1 The stem cell niche .......................................................................................................... 17 2.2.2 Cell differentiation ........................................................................................................... 18 2.3 Plasticity of intestinal stem cells ........................................................................................ 19 2.3.1 Crypt base columnar cells ............................................................................................... 19 2.3.2 Quiescent stem cells......................................................................................................... 21 2.4 Cancer stem cells ................................................................................................................ 22 2.5 Intestinal organoid cultures as model system of the intestine ............................................ 23 3. HOMEOBOX TRANSCRIPTION FACTOR PROX1 IN CELL FATE DECISIONS AND CANCER .............. 23 3.1 Structure of PROX1 ............................................................................................................ 23 3.2 Function of PROX1 in cell fate decisions ........................................................................... 24 3.3 Oncogenic and tumor suppressor functions of PROX1 ...................................................... 24 AIM OF THE STUDY ..................................................................................................................... 27 MATERIALS AND METHODS..................................................................................................... 28 1. MATERIALS ................................................................................................................................ 28 2. METHODS ................................................................................................................................... 37 RESULTS AND DISCUSSION....................................................................................................... 45 ONCOGENIC MUTATIONS IN INTESTINAL ADENOMAS REGULATE BIM-MEDIATED APOPTOSIS INDUCED BY TGF-Β (I) ................................................................................................................... 45 PROX1 PROMOTES EXPANSION OF THE COLORECTAL CANCER STEM CELL POPULATION TO FUEL ... 48 TUMOR GROWTH AND ISCHEMIA RESISTANCE (II) .......................................................................... 48 TRANSCRIPTION FACTOR PROX1 SUPPRESSES NOTCH PATHWAY ACTIVATION VIA THE NUCLEOSOME REMODELLING AND DEACETYLASE COMPLEX IN COLORECTAL CANCER STEM-LIKE CELLS (III) ...................................................................................................................................... 52 CONCLUSIONS AND FUTURE PROSPECTS ........................................................................... 56 ACKNOWLEDGMENTS ............................................................................................................... 58 REFERENCES ................................................................................................................................
Load more

Recommended publications
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
    Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated.
    [Show full text]
  • Supplemental Materials ZNF281 Enhances Cardiac Reprogramming
    Supplemental Materials ZNF281 enhances cardiac reprogramming by modulating cardiac and inflammatory gene expression Huanyu Zhou, Maria Gabriela Morales, Hisayuki Hashimoto, Matthew E. Dickson, Kunhua Song, Wenduo Ye, Min S. Kim, Hanspeter Niederstrasser, Zhaoning Wang, Beibei Chen, Bruce A. Posner, Rhonda Bassel-Duby and Eric N. Olson Supplemental Table 1; related to Figure 1. Supplemental Table 2; related to Figure 1. Supplemental Table 3; related to the “quantitative mRNA measurement” in Materials and Methods section. Supplemental Table 4; related to the “ChIP-seq, gene ontology and pathway analysis” and “RNA-seq” and gene ontology analysis” in Materials and Methods section. Supplemental Figure S1; related to Figure 1. Supplemental Figure S2; related to Figure 2. Supplemental Figure S3; related to Figure 3. Supplemental Figure S4; related to Figure 4. Supplemental Figure S5; related to Figure 6. Supplemental Table S1. Genes included in human retroviral ORF cDNA library. Gene Gene Gene Gene Gene Gene Gene Gene Symbol Symbol Symbol Symbol Symbol Symbol Symbol Symbol AATF BMP8A CEBPE CTNNB1 ESR2 GDF3 HOXA5 IL17D ADIPOQ BRPF1 CEBPG CUX1 ESRRA GDF6 HOXA6 IL17F ADNP BRPF3 CERS1 CX3CL1 ETS1 GIN1 HOXA7 IL18 AEBP1 BUD31 CERS2 CXCL10 ETS2 GLIS3 HOXB1 IL19 AFF4 C17ORF77 CERS4 CXCL11 ETV3 GMEB1 HOXB13 IL1A AHR C1QTNF4 CFL2 CXCL12 ETV7 GPBP1 HOXB5 IL1B AIMP1 C21ORF66 CHIA CXCL13 FAM3B GPER HOXB6 IL1F3 ALS2CR8 CBFA2T2 CIR1 CXCL14 FAM3D GPI HOXB7 IL1F5 ALX1 CBFA2T3 CITED1 CXCL16 FASLG GREM1 HOXB9 IL1F6 ARGFX CBFB CITED2 CXCL3 FBLN1 GREM2 HOXC4 IL1F7
    [Show full text]
  • Genome-Wide DNA Methylation Analysis of KRAS Mutant Cell Lines Ben Yi Tew1,5, Joel K
    www.nature.com/scientificreports OPEN Genome-wide DNA methylation analysis of KRAS mutant cell lines Ben Yi Tew1,5, Joel K. Durand2,5, Kirsten L. Bryant2, Tikvah K. Hayes2, Sen Peng3, Nhan L. Tran4, Gerald C. Gooden1, David N. Buckley1, Channing J. Der2, Albert S. Baldwin2 ✉ & Bodour Salhia1 ✉ Oncogenic RAS mutations are associated with DNA methylation changes that alter gene expression to drive cancer. Recent studies suggest that DNA methylation changes may be stochastic in nature, while other groups propose distinct signaling pathways responsible for aberrant methylation. Better understanding of DNA methylation events associated with oncogenic KRAS expression could enhance therapeutic approaches. Here we analyzed the basal CpG methylation of 11 KRAS-mutant and dependent pancreatic cancer cell lines and observed strikingly similar methylation patterns. KRAS knockdown resulted in unique methylation changes with limited overlap between each cell line. In KRAS-mutant Pa16C pancreatic cancer cells, while KRAS knockdown resulted in over 8,000 diferentially methylated (DM) CpGs, treatment with the ERK1/2-selective inhibitor SCH772984 showed less than 40 DM CpGs, suggesting that ERK is not a broadly active driver of KRAS-associated DNA methylation. KRAS G12V overexpression in an isogenic lung model reveals >50,600 DM CpGs compared to non-transformed controls. In lung and pancreatic cells, gene ontology analyses of DM promoters show an enrichment for genes involved in diferentiation and development. Taken all together, KRAS-mediated DNA methylation are stochastic and independent of canonical downstream efector signaling. These epigenetically altered genes associated with KRAS expression could represent potential therapeutic targets in KRAS-driven cancer. Activating KRAS mutations can be found in nearly 25 percent of all cancers1.
    [Show full text]
  • Investigation of the Underlying Hub Genes and Molexular Pathogensis in Gastric Cancer by Integrated Bioinformatic Analyses
    bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Investigation of the underlying hub genes and molexular pathogensis in gastric cancer by integrated bioinformatic analyses Basavaraj Vastrad1, Chanabasayya Vastrad*2 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Abstract The high mortality rate of gastric cancer (GC) is in part due to the absence of initial disclosure of its biomarkers. The recognition of important genes associated in GC is therefore recommended to advance clinical prognosis, diagnosis and and treatment outcomes. The current investigation used the microarray dataset GSE113255 RNA seq data from the Gene Expression Omnibus database to diagnose differentially expressed genes (DEGs). Pathway and gene ontology enrichment analyses were performed, and a proteinprotein interaction network, modules, target genes - miRNA regulatory network and target genes - TF regulatory network were constructed and analyzed. Finally, validation of hub genes was performed. The 1008 DEGs identified consisted of 505 up regulated genes and 503 down regulated genes.
    [Show full text]
  • Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
    Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase
    [Show full text]
  • “The Interplay of Central and Peripheral Circadian Clocks in White Adipose Function and Metabolic Homeostasis”
    From the Institute of Neurobiology of the University of Lübeck Director: Prof. Dr. rer. nat. H. Oster “The interplay of central and peripheral circadian clocks in white adipose function and metabolic homeostasis” Dissertation for Fulfillment of Requirements for the Doctoral Degree of the University of Lübeck from the Department of Natural Sciences Submitted by Isa Kolbe from Geesthacht, Germany Lübeck 2017 1 First referee: Prof. Dr. rer. nat. Henrik Oster Second referee: Prof. Dr. rer. nat. Stefan Anemüller Date of oral examination: 13. Oktober 2017 Approved for printing. Lübeck, 24. Oktober 2017 2 Declaration Herewith, I confirm that I have written the present PhD thesis independently and with no other sources and aids than quoted. Lübeck, Mai 2017 Isa Kolbe 3 “Zeit. Es gibt Kalender und Uhren, um sie zu messen, aber das will wenig besagen, denn jeder weiß, dass einem eine einzige Stunde wie eine Ewigkeit vorkommen kann, mitunter kann sie aber auch wie in einem Augenblick vergehen – je nachdem, was man in dieser Stunde erlebt. Denn Zeit ist Leben” Michael Ende 4 Contents Summary ........................................................................................................................................ 8 Zusammenfassung ..................................................................................................................... 10 Abbreviations ............................................................................................................................... 12 1 Introduction ..............................................................................................................................
    [Show full text]
  • Administration of Vitamin D Metabolites Affects RNA Expression of Xenobiotic Metabolising Enzymes and Function of ABC Transporters in Rats
    Hindawi Journal of Chemistry Volume 2019, Article ID 1279036, 11 pages https://doi.org/10.1155/2019/1279036 Research Article Administration of Vitamin D Metabolites Affects RNA Expression of Xenobiotic Metabolising Enzymes and Function of ABC Transporters in Rats Karoline Klumpp,1 Frauke Lange,1 Alexandra S. Muscher-Banse,1 Nadine Schnepel,1 Kathrin Hansen,1 Adrian L. Lifschitz,2 Laura Mate,2 and Mirja R. Wilkens 1 1Institute of Physiology and Cell Biology, University of Veterinary Medicine Hannover, Hanover, Germany 2Laboratory of Veterinary Pharmacology, Faculty of Veterinary Sciences, National University of Central Buenos Aires Province, B7000 Tandil, Argentina Correspondence should be addressed to Mirja R. Wilkens; [email protected] Received 6 June 2019; Accepted 13 September 2019; Published 9 October 2019 Guest Editor: Ciriaco Carru Copyright © 2019 Karoline Klumpp et al. -is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. From studies on different species and in cell culture systems, it has been suggested that vitamin D metabolites might affect the metabolism and elimination of xenobiotics. Although most studies performed on rodents and cell cultures report an upregulation of respective enzymes and transporters, data from the literature are inconsistent. Especially results obtained with sheep differ from these observations. As vitamin D metabolites are widely used as feed additives or therapeutics in livestock animals, we aimed to assess whether these differences indicate species-specific responses or occurred due to the very high dosages used in the rodent studies.
    [Show full text]
  • SUPPLEMENTARY DATA Supplementary Table 1. Top Ten
    SUPPLEMENTARY DATA Supplementary Table 1. Top ten most highly expressed protein-coding genes in the EndoC-βH1 cell line. Expression levels provided for non-mitochondrial genes in EndoC-βH1 and the corresponding expression levels in sorted primary human β-cells (1). Ensembl gene ID Gene Name EndoC-βH1 [RPKM] Primary β cells [RPKM] ENSG00000254647.2 INS 8012.452 166347.111 ENSG00000087086.9 FTL 3090.7454 2066.464 ENSG00000100604.8 CHGA 2853.107 1113.162 ENSG00000099194.5 SCD 1411.631 238.714 ENSG00000118271.5 TTR 1312.8928 1488.996 ENSG00000184009.5 ACTG1 1108.0277 839.681 ENSG00000124172.5 ATP5E 863.42334 254.779 ENSG00000156508.13 EEF1A1 831.17316 637.281 ENSG00000112972.10 HMGCS1 719.7504 22.104 ENSG00000167552.9 TUBA1A 689.1415 511.699 ©2016 American Diabetes Association. Published online at http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db16-0361/-/DC1 SUPPLEMENTARY DATA Supplementary Table 2. List of genes selected for inclusion in the primary screen. Expression levels in EndoC-βH1 and sorted primary human β-cells are shown for all genes targeted for silencing in the primary screen, ordered by locus association (1). For gene selection, the following criteria were applied: we first considered (1) all protein-coding genes within 1 Mb of a type 2 diabetes association signal that (2) had non-zero expression (RPKM > 0) in both EndoC-βH1 and primary human β-cells. Previous studies have shown cis-eQTLs to form a relatively tight, symmetrical distribution around the target-gene transcription start site, and a 1 Mb cut-off is thus likely to capture most effector transcripts subject to cis regulation (2-5).
    [Show full text]
  • Placental Epigenetic Biomarkers for the Detection of Isolated Ventricular Septal Defect
    16th World Congress in Fetal Medicine Placental epigenetic biomarkers for the detection of isolated ventricular septal defect Bahado-Singh RO, Zafra R, Albayrak S, Alosh B, Avinash M , Saiyed NM, Mishra NK, Guda C, Rouba A, Radhakrishna U Beaumont Health / Oakland University School of Medicine, Royal Oak, MI, United States Objective Epigenetics refers to molecular mechanisms for controlling gene function that are not due to gene mutations. DNA methylation is the most studied epigenetic mechanism and it involves the chemical binding of single carbon atoms (“methyl groups”) to cytosine nucleotides in DNA. When this methylation occurs in the cytosines of the coding region of a gene, repression of gene transcription classically results. Environmental factors such as diet, smoking and alcohol exposure profoundly affect DNA methylation. VSD is the most common congenital major heart defect (CHD). The pathogenesis of isolated non-syndromic VSD is largely unknown and population-based screening continues to show low prenatal detection rates using ultrasound. Our prior pilot studies using newborn leucocyte DNA found significant changes in DNA methylation in different types of CHD. Further, screening based on these methylation changes achieved high diagnostic accuracy for CHD detection. This study aims to develop DNA methylation-based molecular biomarkers, using placental tissue ,for VSD detection. Additionally, we wanted to use this epigenetic analysis to investigate the pathogenic mechanisms of isolated VSD. Methods The Illumina HumanMethylation450 BeadChip assay was used to measure genome wide DNA methylation level in >450,000 cytosine nucleotide loci in approximately 20,000 genes. Placental specimens from 8 isolated non-syndromic VSD cases and 10 unaffected controls were compared.
    [Show full text]
  • Supplemental Table S1. Primers for Sybrgreen Quantitative RT-PCR Assays
    Supplemental Table S1. Primers for SYBRGreen quantitative RT-PCR assays. Gene Accession Primer Sequence Length Start Stop Tm GC% GAPDH NM_002046.3 GAPDH F TCCTGTTCGACAGTCAGCCGCA 22 39 60 60.43 59.09 GAPDH R GCGCCCAATACGACCAAATCCGT 23 150 128 60.12 56.52 Exon junction 131/132 (reverse primer) on template NM_002046.3 DNAH6 NM_001370.1 DNAH6 F GGGCCTGGTGCTGCTTTGATGA 22 4690 4711 59.66 59.09% DNAH6 R TAGAGAGCTTTGCCGCTTTGGCG 23 4797 4775 60.06 56.52% Exon junction 4790/4791 (reverse primer) on template NM_001370.1 DNAH7 NM_018897.2 DNAH7 F TGCTGCATGAGCGGGCGATTA 21 9973 9993 59.25 57.14% DNAH7 R AGGAAGCCATGTACAAAGGTTGGCA 25 10073 10049 58.85 48.00% Exon junction 9989/9990 (forward primer) on template NM_018897.2 DNAI1 NM_012144.2 DNAI1 F AACAGATGTGCCTGCAGCTGGG 22 673 694 59.67 59.09 DNAI1 R TCTCGATCCCGGACAGGGTTGT 22 822 801 59.07 59.09 Exon junction 814/815 (reverse primer) on template NM_012144.2 RPGRIP1L NM_015272.2 RPGRIP1L F TCCCAAGGTTTCACAAGAAGGCAGT 25 3118 3142 58.5 48.00% RPGRIP1L R TGCCAAGCTTTGTTCTGCAAGCTGA 25 3238 3214 60.06 48.00% Exon junction 3124/3125 (forward primer) on template NM_015272.2 Supplemental Table S2. Transcripts that differentiate IPF/UIP from controls at 5%FDR Fold- p-value Change Transcript Gene p-value p-value p-value (IPF/UIP (IPF/UIP Cluster ID RefSeq Symbol gene_assignment (Age) (Gender) (Smoking) vs. C) vs. C) NM_001178008 // CBS // cystathionine-beta- 8070632 NM_001178008 CBS synthase // 21q22.3 // 875 /// NM_0000 0.456642 0.314761 0.418564 4.83E-36 -2.23 NM_003013 // SFRP2 // secreted frizzled- 8103254 NM_003013
    [Show full text]
  • Transdifferentiation to Pancreatic Progenitors
    UNIVERSITY OF MANCHESTER Transdifferentiation to pancreatic progenitors A thesis submitted to the University of Manchester for the degree of Doctor of Philosophy in the Faculty of Biology, Medicine and Health 2019 Stephen John Wearne School of Medical Sciences Division of Diabetes Endocrinology and Gastroenterology Contents Contents..................................................................................................................................................................................... 1 List of tables ............................................................................................................................................................................... 3 List of figures.............................................................................................................................................................................. 4 List of abbreviations ................................................................................................................................................................... 5 List of publications ..................................................................................................................................................................... 8 Abstract ...................................................................................................................................................................................... 9 Declaration ..............................................................................................................................................................................
    [Show full text]
  • Overview Gene List Target Scan Vs DIANA Group a Group B Group A
    Overview Gene list Target scan vs DIANA Group A Group B Group A hsa-miR-181a hsa-miR-323 hsa-miR-326 Target scan Diana microT Overlap Target scan Diana microT Overlap Target scan SEPT3 SEPT3 SEPT3 SEPT7 ADARB1 HPCAL4 ABHD2 ABL2 ABHD13 ACVR2A ADCYAP1R1 AKAP13 PDPK1 ACRBP ACAN ABI1 ADAMTS1 ALAD APOBR ACVRL1 ACCN2 ABLIM1 ADAMTSL1 ANKRD52 ATXN1 ADAM19 ACER3 ACSL1 AKAP7 ARID2 C18orf23,RNF165 ADAM33 ACVR2A ACTN2 ANKRD43 ARL3 C20orf29 ADAMTS2 ADAMTS1 ACVR2A AP1S3 ARRB1 CACNG4 AHCYL2 ADAMTS18 ACVR2B ARID2 BBC3 CCNJL ALOX15B ADAMTS5 ADAM11 ATP11A BTG1 CYP2E1 ANK1 ADAMTSL1 ADAM22 ATXN1 C18orf62 GNB1L ANKS6 ADARB1 ADAMTS1 B4GALT1 C1orf21 GPR61 APBA1 AFAP1 ADAMTS6 BAG4 CADM4 GTSE1 ARCN1 AFTPH ADAMTSL1 BAI3 CALML4 HPCAL4 ARHGEF37 AK3 ADCY9 BNC2 CAPN6 KIAA0152 ARID3B AKAP7 ADRBK1 BRD1 CBFA2T2 KIF1A ARL8A ANAPC16 AFF2 BRWD1 CEBPA MACF1 ATP2B2 ANK1 AHCTF1,AHCTF1PBTBD3 CHD1 MYO1D ATP6V1G2 ANKRD12 AKAP2,PALM2 C13orf23 CIT PCNT AUP1 ANKRD33B AKAP6 C14orf43 CLASP2 PDPK1 BCL2L2 ANKRD43 AKAP7 CAPRIN1 CLCN5 PLEKHG4B BHLHE40 ANKRD44 AKAP9 CARM1 CLIP3 PPARA BTBD3 ANKRD52 AKT3 CBX4 COL5A2 PRB1,PRB2,PRB4 BTRC AP1S3 ALG9 CCDC117 CTNS PTPRT C10orf26 APBA1 ANKRD13C CCNJ DCTN4 PYCR1 C14orf1 APLP2 ANKRD20B CDH13 DCUN1D4 RAPGEF1 C16orf45 APOO ANKRD43 CDON DDB1 SRCAP C16orf54 ARID2 ANKRD50 CDYL DDX39B TMEM63C C1orf106 ARL3 AP1G1 CEP350 DIP2C C1orf27 ARRDC3 AP1S3 CHD7 DNAJB3 C22orf29 ATF7 API5 CHIC1 EEPD1 C9orf3 ATG2B ARFGEF2 CLIP1 EIF2C1 CACNA1E ATG7 ARHGAP12 CNOT6L ELFN2 CAPN12 ATP11A ARHGAP26 CNR1 ELK1 CASKIN1 ATP2B3 ARHGAP29 CNTN4 FAM172A CBFA2T3 ATP8B2 ARHGEF3 CNTNAP2
    [Show full text]